Maria Lomeli Garcia
I am a research scientist at Babylon Health, UK. Previously, I was a research associate, working with Zoubin Ghahramani at the Machine Learning group, CBL, University of Cambridge and member of Trinity Hall college.
I studied my PhD at the Gatsby Unit, UCL, my supervisor was Yee Whye Teh. Before coming to the UK, I did an MSc in Mathematical Sciences at IIMAS,
Universidad Nacional Autónoma de México, advised by Ramsés Mena.
- Machine Learning for Healthcare
- Bayesian Nonparametrics
- Reproducing kernel Hilbert spaces
- Exact inference methods (MCMC, SMC)
Lomeli, M., Rowland, M., Gretton, A. and Ghahramani, Z., ''Antithetic and Monte Carlo kernel estimators for partial rankings'', Statistics and Computing, 2019 (to appear), available online.
Lomeli, M., Favaro, S., Teh, Y. W.,'' A marginal sampler for -Stable Poisson-Kingman mixture models'', Journal of Computational and Graphical Statistics, 2017, Vol 26, 44-53 JCGS.
Favaro, S., Lomeli, M., Nipoti, B., Teh, Y.W., ''Stick-breaking representations of -stable Poisson-Kingman models'' , Electronic Journal of Statistics, 2014, Vol. 8, pp 1063-1085 EJS.
Favaro, S., Lomeli, M., Teh, Y.W.,''On a class of -stable Poisson-Kingman models and an effective marginalized sampler'', Statistics and Computing, 2014, Vol 25, pp 67-78
Lomeli, M., Favaro, S.,Teh, Y.W., 2015, ''A hybrid sampler for Poisson-Kingman mixture models'', Neural information Processing Systems NIPS
Sejdinovic, D., Strathmann, H., Lomeli Garcia, M., Andrieu, C., Gretton, A., 2014,''Kernel Adaptive Metropolis-Hastings'', International Conference in Machine Learning ICML
Bloem-Reddy, B., Mathieu, E., Foster, A., Rainforth, T., Ge, H., Lomeli, M., Ghahramani, Z., Teh, Y.W., 2017, ''Sampling and inference for discrete random probability measures in probabilistic programs'', Approximate Inference workshop, NIPS
Theses and projects
General Bayesian inference schemes in infinite mixture models
PhD thesis, University College London
Arxiv version: arXiv:1702.08781
(Figures 2.2 and 5.1 are not displayed properly, email me for the pdf version)
Consistencia Posterior de Modelos Bayesianos No Paramétricos
(Posterior Consistency of Bayesian Nonparametric Models)
MSc project, UNAM
Available upon request (In Spanish)
Qué tan "expertos" son los Expertos: Un Modelo de Evaluación y Pronóstico
Undergraduate thesis, ITAM
Available upon request (In Spanish)
Walecki, R., Buchard, A., Gourgoulias, K., Hart, C., Lomeli, M., Navarro, A. K. W., Zwiessele, M., Johri, S. ''Universal marginaliser for amortised inference and embeddings of generative models'', arXiv preprint.
Valera, I., Pradier, M., Lomeli, M. and Ghahramani, Z., ''General Latent Feature Model for Heterogeneous Datasets'', arXiv preprint.
June, 2019. Talk at ''Congreso Bayesiano de América Latina'', Peru
March, 2019. Talk at the Statistics seminar series, Queen Mary University, London
June 11, 2018. Talk at Parallelizing Monte Carlo Algorithms workshop, School of Mathematics, University of Bristol
March 15, 2018. Talk at the CamAIML event, Microsoft research Cambridge
February 16, 2018. Talk at the University of Glasgow, Statistics seminar
Febryary 2, 2018. Talk at UCL, CSML Lunchtime seminar
February 1, 2018. Talk at Amazon Cambridge research series seminar
August 30, 2017. Talk at the 2017 SMC workshop
June 7, 2017. Talk at the ''Congreso Bayesiano de América Latina''
June 14, 2016. Talk at the ''Bayes Legacy'' sesssion, 13th ISBA Wold meeting in Sardinia, Italy
June 2, 2016. Talk at the Machine Learning group, CBL, University of Cambridge
May 5, 2016. Talk at Machine Learning reading group, CBL, University of Cambridge
July 16, 2015. Talk at CBL, University of Cambridge
June 22, 2015. Talk at the 10th
Bayesian Nonparametrics conference
June 15, 2015. Talk at the 9th
Bayesian Inference for Stochastic Processes conference
January 26, 2014. Talk
at the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas,
Universidad Nacional Autónoma de México
October 24, 2014. Talk
at the Computational Statistics seminar, University of Oxford
September 24, 2014. Talk
at CBL, University of Cambridge
March 3, 2014. Talk at the workshop
Advances in Scalable Bayesian Computation, available
Teaching assistant, Part II Statistical modelling course, Statslab, University of Cambridge (Lent, 2018)
Coding lab demonstrator, APTS, Statistical computing module for Statistics PhD students, University of Cambridge (December, 2017)
Coding lab demonstrator, MLSALT1 graduate course, University of Cambridge (Michaelmas, 2017)
Coding lab demonstrator, 3F8 undergraduate course, University of Cambridge (Lent, 2017)
Teaching assistant, Statistical Data Mining and Machine Learning MSc in Applied Statistics course, University of Oxford (Hilary term 2014 and 2015)
Coding lab demonstrator, Kernel methods module, Introduction to machine learning graduate course, University College London (2013)
Teaching assistant, Probabilistic and Unsupervised Learning graduate course, University College London (Autumn, 2012)
Lecturer, Stochastic Processes, undergraduate course, Instituto Tecnológico Autónomo de México (Summer, 2011)
2019, Uncertainty in Artificial Intelligence conference
2018, Bayesian Analysis
2018, Journal of Machine Learning Research
2017, Scandinavian Journal of Statistics
2016, Computational Statistics and Data Analysis
2016, Statistics and Computing
2016, 2017, 2019 International Conference in Machine Learning
2013, 2014, 2015, 2017, 2018 Neural Information Processing Systems
I was one of the organisers of our CSML Lunch Talk Series.